import numpy as np
import pandas as pd
from pandas import Series, DataFrame

import chart_studio
import chart_studio.plotly as py
import chart_studio.tools as tls


import matplotlib.pyplot as plt
from matplotlib.ticker import ScalarFormatter, FormatStrFormatter

import seaborn as sns
sns.set()

import plotly
import plotly.graph_objs as go#graph objects
import plotly.offline as offline
offline.init_notebook_mode(connected=True)

%matplotlib inline
plt.style.use("Solarize_Light2")

# Read the data of all the districts for  Condominiums and Apartments in Singapore(2017 to 2022)
df_singapore_condo_apt_data = pd.read_csv('singapore_districts_combined_dataset_condo_apt.csv') 

# delete S/N column
del df_singapore_condo_apt_data['S/N']

# Sort the price of property in ascending order according to Unit Price($psf).
df_singapore_condo_apt_data.sort_values(["Unit Price ($psf)"],axis=0,ascending=[False],inplace=True) 

# Describe the dataset
df_singapore_condo_apt_data.describe()

	Postal District	No. of Units	Price ($)	Area (Sqft)	Unit Price ($psf)
count	105934.000000	105934.000000	1.059340e+05	105934.000000	105934.000000
mean	13.865747	1.061718	1.792358e+06	1151.924245	1562.789916
std	6.810216	3.640613	9.667202e+06	6129.392036	540.717538
min	1.000000	1.000000	3.400000e+05	258.000000	280.000000
25%	9.000000	1.000000	1.000000e+06	678.000000	1197.000000
50%	15.000000	1.000000	1.323000e+06	947.000000	1491.000000
75%	19.000000	1.000000	1.776000e+06	1259.000000	1799.000000
max	28.000000	560.000000	9.800000e+08	947081.000000	6210.000000

# Returns the first 5 rows of dataset
df_singapore_condo_apt_data.head()

	Project Name	Street Name	Type	Postal District	Market Segment	Tenure	Type of Sale	No. of Units	Price ($)	Nett Price ($)	Area (Sqft)	Type of Area	Floor Level	Unit Price ($psf)	Date of Sale
22566	LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	75000000	-	12077	Strata	01 to 05	6210	Oct-21
25439	EDEN	DRAYCOTT PARK	Apartment	10	CCR	Freehold	Resale	1	18287000	-	3035	Strata	21 to 25	6024	Mar-21
24587	LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	39000000	-	6577	Strata	01 to 05	5930	May-21
24549	PARK NOVA	TOMLINSON ROAD	Apartment	10	CCR	Freehold	New Sale	1	34438000	-	5899	Strata	16 to 20	5838	May-21
21278	LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	35000000	-	6049	Strata	01 to 05	5786	Dec-21

# datasets has 105934 rows and 15 columns
df_singapore_condo_apt_data.shape 

(105934, 15)

# Print a summary of the dataframe
df_singapore_condo_apt_data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 105934 entries, 22566 to 61963
Data columns (total 15 columns):
 #   Column             Non-Null Count   Dtype 
---  ------             --------------   ----- 
 0   Project Name       105934 non-null  object
 1   Street Name        105934 non-null  object
 2   Type               105934 non-null  object
 3   Postal District    105934 non-null  int64 
 4   Market Segment     105934 non-null  object
 5   Tenure             105932 non-null  object
 6   Type of Sale       105934 non-null  object
 7   No. of Units       105934 non-null  int64 
 8   Price ($)          105934 non-null  int64 
 9   Nett Price ($)     105934 non-null  object
 10  Area (Sqft)        105934 non-null  int64 
 11  Type of Area       105934 non-null  object
 12  Floor Level        105934 non-null  object
 13  Unit Price ($psf)  105934 non-null  int64 
 14  Date of Sale       105934 non-null  object
dtypes: int64(5), object(10)
memory usage: 12.9+ MB

# Observe the corelations within the dataframe
df_singapore_condo_apt_data.corr() 

	Postal District	No. of Units	Price ($)	Area (Sqft)	Unit Price ($psf)
Postal District	1.000000	-0.003214	-0.035954	-0.005095	-0.473432
No. of Units	-0.003214	1.000000	0.910905	0.985778	0.005612
Price ($)	-0.035954	0.910905	1.000000	0.930535	0.081127
Area (Sqft)	-0.005095	0.985778	0.930535	1.000000	-0.002421
Unit Price ($psf)	-0.473432	0.005612	0.081127	-0.002421	1.000000

# Create the corerelation matrix using seaborn
sns.heatmap(df_singapore_condo_apt_data.corr(),cmap='YlGn',annot=True)

plt.title('Singapore Condominiums and Apartments correlation matrix')

Text(0.5, 1.0, 'Singapore Condominiums and Apartments correlation matrix')

Data Cleaning - Missing Entries

# Check for missing entries
df_singapore_condo_apt_data.isna().sum()

Project Name         0
Street Name          0
Type                 0
Postal District      0
Market Segment       0
Tenure               2
Type of Sale         0
No. of Units         0
Price ($)            0
Nett Price ($)       0
Area (Sqft)          0
Type of Area         0
Floor Level          0
Unit Price ($psf)    0
Date of Sale         0
dtype: int64

Since 'Tenure' is in string format, a check is done to see which type of tenure has the most of number of units.

 df_tenure = df_singapore_condo_apt_data.groupby(['Tenure'])['No. of Units'].sum().reset_index()
df_tenure.head(5)

	Tenure	No. of Units
0	100 yrs lease commencing from 1986	294
1	101 yrs lease commencing from 1985	565
2	102 yrs lease commencing from 1977	7
3	102 yrs lease commencing from 1978	36
4	102 yrs lease commencing from 1986	330

# Dataframe for 'Tenure' will be sorted to find the most common item in 'Tenure'.
df_tenure_sort = df_tenure.sort_values(["No. of Units"],axis=0,ascending=[False]) # Finding Most Common Item in Tenure 
df_tenure_sort.head(10)

	Tenure	No. of Units
121	Freehold	32923
70	99 yrs lease commencing from 2018	15263
71	99 yrs lease commencing from 2019	6263
69	99 yrs lease commencing from 2017	5485
68	99 yrs lease commencing from 2016	4708
63	99 yrs lease commencing from 2011	4607
64	99 yrs lease commencing from 2012	3438
67	99 yrs lease commencing from 2015	3301
66	99 yrs lease commencing from 2014	3044
62	99 yrs lease commencing from 2010	2685

# Replacement for missing entries
df_singapore_condo_apt_data['Tenure'].fillna(value="Freehold", inplace= True) 
df_singapore_condo_apt_data

	Project Name	Street Name	Type	Postal District	Market Segment	Tenure	Type of Sale	No. of Units	Price ($)	Nett Price ($)	Area (Sqft)	Type of Area	Floor Level	Unit Price ($psf)	Date of Sale
22566	LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	75000000	-	12077	Strata	01 to 05	6210	Oct-21
25439	EDEN	DRAYCOTT PARK	Apartment	10	CCR	Freehold	Resale	1	18287000	-	3035	Strata	21 to 25	6024	Mar-21
24587	LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	39000000	-	6577	Strata	01 to 05	5930	May-21
24549	PARK NOVA	TOMLINSON ROAD	Apartment	10	CCR	Freehold	New Sale	1	34438000	-	5899	Strata	16 to 20	5838	May-21
21278	LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	35000000	-	6049	Strata	01 to 05	5786	Dec-21
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
91278	MAYFAIR PARK	JALAN BINGKA	Apartment	21	RCR	99 yrs lease commencing from 1952	Resale	1	400000	-	915	Strata	01 to 05	437	Jun-21
60017	RESIDENTIAL APARTMENTS	BALESTIER ROAD	Apartment	12	RCR	9999 yrs lease commencing from 1961	Resale	1	820000	-	1900	Strata	01 to 05	432	Mar-17
46345	RESIDENTIAL APARTMENTS	JOO CHIAT TERRACE	Apartment	15	RCR	Freehold	Resale	1	505000	-	1195	Strata	01 to 05	423	Jan-20
35044	RESIDENTIAL APARTMENTS	SING JOO WALK	Apartment	8	RCR	Freehold	Resale	1	525000	-	1499	Strata	01 to 05	350	Jul-17
61963	BEDOK SHOPPING COMPLEX	BEDOK ROAD	Apartment	16	OCR	60 yrs lease commencing from 1977	Resale	1	340000	-	1216	Strata	01 to 05	280	Oct-21

105934 rows × 15 columns

# Check for replacement in missing entries
check_for_replacement = df_singapore_condo_apt_data.isna().sum()
print(check_for_replacement) 

Project Name         0
Street Name          0
Type                 0
Postal District      0
Market Segment       0
Tenure               0
Type of Sale         0
No. of Units         0
Price ($)            0
Nett Price ($)       0
Area (Sqft)          0
Type of Area         0
Floor Level          0
Unit Price ($psf)    0
Date of Sale         0
dtype: int64

Converting date and time within dataframe.

# Check the data type of each column.
print (df_singapore_condo_apt_data)
print (df_singapore_condo_apt_data.dtypes)

                 Project Name        Street Name         Type  \
22566      LES MAISONS NASSIM        NASSIM ROAD  Condominium   
25439                    EDEN      DRAYCOTT PARK    Apartment   
24587      LES MAISONS NASSIM        NASSIM ROAD  Condominium   
24549               PARK NOVA     TOMLINSON ROAD    Apartment   
21278      LES MAISONS NASSIM        NASSIM ROAD  Condominium   
...                       ...                ...          ...   
91278            MAYFAIR PARK       JALAN BINGKA    Apartment   
60017  RESIDENTIAL APARTMENTS     BALESTIER ROAD    Apartment   
46345  RESIDENTIAL APARTMENTS  JOO CHIAT TERRACE    Apartment   
35044  RESIDENTIAL APARTMENTS      SING JOO WALK    Apartment   
61963  BEDOK SHOPPING COMPLEX         BEDOK ROAD    Apartment   

       Postal District Market Segment                               Tenure  \
22566               10            CCR                             Freehold   
25439               10            CCR                             Freehold   
24587               10            CCR                             Freehold   
24549               10            CCR                             Freehold   
21278               10            CCR                             Freehold   
...                ...            ...                                  ...   
91278               21            RCR    99 yrs lease commencing from 1952   
60017               12            RCR  9999 yrs lease commencing from 1961   
46345               15            RCR                             Freehold   
35044                8            RCR                             Freehold   
61963               16            OCR    60 yrs lease commencing from 1977   

      Type of Sale  No. of Units  Price ($) Nett Price ($)  Area (Sqft)  \
22566     New Sale             1   75000000              -        12077   
25439       Resale             1   18287000              -         3035   
24587     New Sale             1   39000000              -         6577   
24549     New Sale             1   34438000              -         5899   
21278     New Sale             1   35000000              -         6049   
...            ...           ...        ...            ...          ...   
91278       Resale             1     400000              -          915   
60017       Resale             1     820000              -         1900   
46345       Resale             1     505000              -         1195   
35044       Resale             1     525000              -         1499   
61963       Resale             1     340000              -         1216   

      Type of Area Floor Level  Unit Price ($psf) Date of Sale  
22566       Strata    01 to 05               6210       Oct-21  
25439       Strata    21 to 25               6024       Mar-21  
24587       Strata    01 to 05               5930       May-21  
24549       Strata    16 to 20               5838       May-21  
21278       Strata    01 to 05               5786       Dec-21  
...            ...         ...                ...          ...  
91278       Strata    01 to 05                437       Jun-21  
60017       Strata    01 to 05                432       Mar-17  
46345       Strata    01 to 05                423       Jan-20  
35044       Strata    01 to 05                350       Jul-17  
61963       Strata    01 to 05                280       Oct-21  

[105934 rows x 15 columns]
Project Name         object
Street Name          object
Type                 object
Postal District       int64
Market Segment       object
Tenure               object
Type of Sale         object
No. of Units          int64
Price ($)             int64
Nett Price ($)       object
Area (Sqft)           int64
Type of Area         object
Floor Level          object
Unit Price ($psf)     int64
Date of Sale         object
dtype: object

# Converting to Month and Year for Date of Sale.
df_singapore_condo_apt_data['Date of Sale'] = pd.to_datetime(df_singapore_condo_apt_data['Date of Sale'], format='%b-%y')

print (df_singapore_condo_apt_data)
print (df_singapore_condo_apt_data.dtypes)

                 Project Name        Street Name         Type  \
22566      LES MAISONS NASSIM        NASSIM ROAD  Condominium   
25439                    EDEN      DRAYCOTT PARK    Apartment   
24587      LES MAISONS NASSIM        NASSIM ROAD  Condominium   
24549               PARK NOVA     TOMLINSON ROAD    Apartment   
21278      LES MAISONS NASSIM        NASSIM ROAD  Condominium   
...                       ...                ...          ...   
91278            MAYFAIR PARK       JALAN BINGKA    Apartment   
60017  RESIDENTIAL APARTMENTS     BALESTIER ROAD    Apartment   
46345  RESIDENTIAL APARTMENTS  JOO CHIAT TERRACE    Apartment   
35044  RESIDENTIAL APARTMENTS      SING JOO WALK    Apartment   
61963  BEDOK SHOPPING COMPLEX         BEDOK ROAD    Apartment   

       Postal District Market Segment                               Tenure  \
22566               10            CCR                             Freehold   
25439               10            CCR                             Freehold   
24587               10            CCR                             Freehold   
24549               10            CCR                             Freehold   
21278               10            CCR                             Freehold   
...                ...            ...                                  ...   
91278               21            RCR    99 yrs lease commencing from 1952   
60017               12            RCR  9999 yrs lease commencing from 1961   
46345               15            RCR                             Freehold   
35044                8            RCR                             Freehold   
61963               16            OCR    60 yrs lease commencing from 1977   

      Type of Sale  No. of Units  Price ($) Nett Price ($)  Area (Sqft)  \
22566     New Sale             1   75000000              -        12077   
25439       Resale             1   18287000              -         3035   
24587     New Sale             1   39000000              -         6577   
24549     New Sale             1   34438000              -         5899   
21278     New Sale             1   35000000              -         6049   
...            ...           ...        ...            ...          ...   
91278       Resale             1     400000              -          915   
60017       Resale             1     820000              -         1900   
46345       Resale             1     505000              -         1195   
35044       Resale             1     525000              -         1499   
61963       Resale             1     340000              -         1216   

      Type of Area Floor Level  Unit Price ($psf) Date of Sale  
22566       Strata    01 to 05               6210   2021-10-01  
25439       Strata    21 to 25               6024   2021-03-01  
24587       Strata    01 to 05               5930   2021-05-01  
24549       Strata    16 to 20               5838   2021-05-01  
21278       Strata    01 to 05               5786   2021-12-01  
...            ...         ...                ...          ...  
91278       Strata    01 to 05                437   2021-06-01  
60017       Strata    01 to 05                432   2017-03-01  
46345       Strata    01 to 05                423   2020-01-01  
35044       Strata    01 to 05                350   2017-07-01  
61963       Strata    01 to 05                280   2021-10-01  

[105934 rows x 15 columns]
Project Name                 object
Street Name                  object
Type                         object
Postal District               int64
Market Segment               object
Tenure                       object
Type of Sale                 object
No. of Units                  int64
Price ($)                     int64
Nett Price ($)               object
Area (Sqft)                   int64
Type of Area                 object
Floor Level                  object
Unit Price ($psf)             int64
Date of Sale         datetime64[ns]
dtype: object

#format to month and year for the Date of Sale
df_singapore_condo_apt_data['Date of Sale'] = pd.to_datetime(df_singapore_condo_apt_data['Date of Sale'], format='%Y%m')

print (df_singapore_condo_apt_data)
print (df_singapore_condo_apt_data.dtypes)

                 Project Name        Street Name         Type  \
22566      LES MAISONS NASSIM        NASSIM ROAD  Condominium   
25439                    EDEN      DRAYCOTT PARK    Apartment   
24587      LES MAISONS NASSIM        NASSIM ROAD  Condominium   
24549               PARK NOVA     TOMLINSON ROAD    Apartment   
21278      LES MAISONS NASSIM        NASSIM ROAD  Condominium   
...                       ...                ...          ...   
91278            MAYFAIR PARK       JALAN BINGKA    Apartment   
60017  RESIDENTIAL APARTMENTS     BALESTIER ROAD    Apartment   
46345  RESIDENTIAL APARTMENTS  JOO CHIAT TERRACE    Apartment   
35044  RESIDENTIAL APARTMENTS      SING JOO WALK    Apartment   
61963  BEDOK SHOPPING COMPLEX         BEDOK ROAD    Apartment   

       Postal District Market Segment                               Tenure  \
22566               10            CCR                             Freehold   
25439               10            CCR                             Freehold   
24587               10            CCR                             Freehold   
24549               10            CCR                             Freehold   
21278               10            CCR                             Freehold   
...                ...            ...                                  ...   
91278               21            RCR    99 yrs lease commencing from 1952   
60017               12            RCR  9999 yrs lease commencing from 1961   
46345               15            RCR                             Freehold   
35044                8            RCR                             Freehold   
61963               16            OCR    60 yrs lease commencing from 1977   

      Type of Sale  No. of Units  Price ($) Nett Price ($)  Area (Sqft)  \
22566     New Sale             1   75000000              -        12077   
25439       Resale             1   18287000              -         3035   
24587     New Sale             1   39000000              -         6577   
24549     New Sale             1   34438000              -         5899   
21278     New Sale             1   35000000              -         6049   
...            ...           ...        ...            ...          ...   
91278       Resale             1     400000              -          915   
60017       Resale             1     820000              -         1900   
46345       Resale             1     505000              -         1195   
35044       Resale             1     525000              -         1499   
61963       Resale             1     340000              -         1216   

      Type of Area Floor Level  Unit Price ($psf) Date of Sale  
22566       Strata    01 to 05               6210   2021-10-01  
25439       Strata    21 to 25               6024   2021-03-01  
24587       Strata    01 to 05               5930   2021-05-01  
24549       Strata    16 to 20               5838   2021-05-01  
21278       Strata    01 to 05               5786   2021-12-01  
...            ...         ...                ...          ...  
91278       Strata    01 to 05                437   2021-06-01  
60017       Strata    01 to 05                432   2017-03-01  
46345       Strata    01 to 05                423   2020-01-01  
35044       Strata    01 to 05                350   2017-07-01  
61963       Strata    01 to 05                280   2021-10-01  

[105934 rows x 15 columns]
Project Name                 object
Street Name                  object
Type                         object
Postal District               int64
Market Segment               object
Tenure                       object
Type of Sale                 object
No. of Units                  int64
Price ($)                     int64
Nett Price ($)               object
Area (Sqft)                   int64
Type of Area                 object
Floor Level                  object
Unit Price ($psf)             int64
Date of Sale         datetime64[ns]
dtype: object

#Check the dataframe for change format in Date of Sale
df_singapore_condo_apt_data.head()

	Project Name	Street Name	Type	Postal District	Market Segment	Tenure	Type of Sale	No. of Units	Price ($)	Nett Price ($)	Area (Sqft)	Type of Area	Floor Level	Unit Price ($psf)	Date of Sale
22566	LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	75000000	-	12077	Strata	01 to 05	6210	2021-10-01
25439	EDEN	DRAYCOTT PARK	Apartment	10	CCR	Freehold	Resale	1	18287000	-	3035	Strata	21 to 25	6024	2021-03-01
24587	LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	39000000	-	6577	Strata	01 to 05	5930	2021-05-01
24549	PARK NOVA	TOMLINSON ROAD	Apartment	10	CCR	Freehold	New Sale	1	34438000	-	5899	Strata	16 to 20	5838	2021-05-01
21278	LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	35000000	-	6049	Strata	01 to 05	5786	2021-12-01

# Perform inserting Year into dataframe
df_singapore_condo_apt_data['Year'] = df_singapore_condo_apt_data['Date of Sale'].dt.year
df_singapore_condo_apt_data.head()

	Project Name	Street Name	Type	Postal District	Market Segment	Tenure	Type of Sale	No. of Units	Price ($)	Nett Price ($)	Area (Sqft)	Type of Area	Floor Level	Unit Price ($psf)	Date of Sale	Year
22566	LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	75000000	-	12077	Strata	01 to 05	6210	2021-10-01	2021
25439	EDEN	DRAYCOTT PARK	Apartment	10	CCR	Freehold	Resale	1	18287000	-	3035	Strata	21 to 25	6024	2021-03-01	2021
24587	LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	39000000	-	6577	Strata	01 to 05	5930	2021-05-01	2021
24549	PARK NOVA	TOMLINSON ROAD	Apartment	10	CCR	Freehold	New Sale	1	34438000	-	5899	Strata	16 to 20	5838	2021-05-01	2021
21278	LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	35000000	-	6049	Strata	01 to 05	5786	2021-12-01	2021

# Performing inserting Month into dataframe
df_singapore_condo_apt_data['Month'] = df_singapore_condo_apt_data['Date of Sale'].dt.month
#df_mth = df_singapore_condo_apt_data.head()
#df_mth
df_singapore_condo_apt_data

	Project Name	Street Name	Type	Postal District	Market Segment	Tenure	Type of Sale	No. of Units	Price ($)	Nett Price ($)	Area (Sqft)	Type of Area	Floor Level	Unit Price ($psf)	Date of Sale	Year	Month
22566	LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	75000000	-	12077	Strata	01 to 05	6210	2021-10-01	2021	10
25439	EDEN	DRAYCOTT PARK	Apartment	10	CCR	Freehold	Resale	1	18287000	-	3035	Strata	21 to 25	6024	2021-03-01	2021	3
24587	LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	39000000	-	6577	Strata	01 to 05	5930	2021-05-01	2021	5
24549	PARK NOVA	TOMLINSON ROAD	Apartment	10	CCR	Freehold	New Sale	1	34438000	-	5899	Strata	16 to 20	5838	2021-05-01	2021	5
21278	LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	35000000	-	6049	Strata	01 to 05	5786	2021-12-01	2021	12
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
91278	MAYFAIR PARK	JALAN BINGKA	Apartment	21	RCR	99 yrs lease commencing from 1952	Resale	1	400000	-	915	Strata	01 to 05	437	2021-06-01	2021	6
60017	RESIDENTIAL APARTMENTS	BALESTIER ROAD	Apartment	12	RCR	9999 yrs lease commencing from 1961	Resale	1	820000	-	1900	Strata	01 to 05	432	2017-03-01	2017	3
46345	RESIDENTIAL APARTMENTS	JOO CHIAT TERRACE	Apartment	15	RCR	Freehold	Resale	1	505000	-	1195	Strata	01 to 05	423	2020-01-01	2020	1
35044	RESIDENTIAL APARTMENTS	SING JOO WALK	Apartment	8	RCR	Freehold	Resale	1	525000	-	1499	Strata	01 to 05	350	2017-07-01	2017	7
61963	BEDOK SHOPPING COMPLEX	BEDOK ROAD	Apartment	16	OCR	60 yrs lease commencing from 1977	Resale	1	340000	-	1216	Strata	01 to 05	280	2021-10-01	2021	10

105934 rows × 17 columns

1. Private Residential Property Price Index Observation

Source - https://data.gov.sg/dataset/private-residential-property-price-index-by-type-of-property

2. Study the trend for transactions in terms of number of units within the districts for last 5 years.

# Study the trend for transactions in terms of number of units within the districts for Year.
df_year = df_singapore_condo_apt_data['Year'].value_counts()
print(df_year)
print("The total number of units sold :", df_singapore_condo_apt_data['Year'].value_counts().sum())

2021    29352
2017    20883
2018    19375
2020    18257
2019    16644
2022     1423
Name: Year, dtype: int64
The total number of units sold : 105934

# Remove the Year 2022 since it has smaller number as compared to other years
df_year_remove = df_singapore_condo_apt_data[(df_singapore_condo_apt_data["Year"] !=2022 )].Year.value_counts().sort_index()
df_year_remove

2017    20883
2018    19375
2019    16644
2020    18257
2021    29352
Name: Year, dtype: int64

# Creating index for plot
df_year_remove.index = ['2017', '2018', '2019', '2020','2021']
df_year_remove

2017    20883
2018    19375
2019    16644
2020    18257
2021    29352
Name: Year, dtype: int64

df_avg_month = df_year_remove.mean()
df_avg_month
print("The average number of transactions for number of units(2017 to 2021) is", df_avg_month)

The average number of transactions for number of units(2017 to 2021) is 20902.2

# Plot the graph to study the trnd in the number of units sold within the years.
df_year_remove.plot(x='Year', figsize=(20,10), title = "Transactions For Number of Units - Year")
plt.ylabel('Number of Units')
plt.xlabel('Year')
plt.text(0,df_year_remove[0],'20883',ha='right',fontsize=20)
plt.text(1,df_year_remove[1],'19375',ha='left',fontsize=20)
plt.text(2,df_year_remove[2],'16644',ha='left',fontsize=20)
plt.text(3,df_year_remove[3],'18257',ha='right',fontsize=20)
plt.text(4,df_year_remove[4],'29352',ha='center',fontsize=20)
plt.axvline(x=3, color='red', linestyle='--')
plt.axhline(y=20902, color='green', linestyle='--')

<matplotlib.lines.Line2D at 0x18d0a6996a0>

# Setting index to 'Project Name'
df_singapore_condo_apt_data.set_index('Project Name')

	Street Name	Type	Postal District	Market Segment	Tenure	Type of Sale	No. of Units	Price ($)	Nett Price ($)	Area (Sqft)	Type of Area	Floor Level	Unit Price ($psf)	Date of Sale	Year	Month
Project Name
LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	75000000	-	12077	Strata	01 to 05	6210	2021-10-01	2021	10
EDEN	DRAYCOTT PARK	Apartment	10	CCR	Freehold	Resale	1	18287000	-	3035	Strata	21 to 25	6024	2021-03-01	2021	3
LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	39000000	-	6577	Strata	01 to 05	5930	2021-05-01	2021	5
PARK NOVA	TOMLINSON ROAD	Apartment	10	CCR	Freehold	New Sale	1	34438000	-	5899	Strata	16 to 20	5838	2021-05-01	2021	5
LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	35000000	-	6049	Strata	01 to 05	5786	2021-12-01	2021	12
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
MAYFAIR PARK	JALAN BINGKA	Apartment	21	RCR	99 yrs lease commencing from 1952	Resale	1	400000	-	915	Strata	01 to 05	437	2021-06-01	2021	6
RESIDENTIAL APARTMENTS	BALESTIER ROAD	Apartment	12	RCR	9999 yrs lease commencing from 1961	Resale	1	820000	-	1900	Strata	01 to 05	432	2017-03-01	2017	3
RESIDENTIAL APARTMENTS	JOO CHIAT TERRACE	Apartment	15	RCR	Freehold	Resale	1	505000	-	1195	Strata	01 to 05	423	2020-01-01	2020	1
RESIDENTIAL APARTMENTS	SING JOO WALK	Apartment	8	RCR	Freehold	Resale	1	525000	-	1499	Strata	01 to 05	350	2017-07-01	2017	7
BEDOK SHOPPING COMPLEX	BEDOK ROAD	Apartment	16	OCR	60 yrs lease commencing from 1977	Resale	1	340000	-	1216	Strata	01 to 05	280	2021-10-01	2021	10

105934 rows × 16 columns

# Calculating % for the most number units sold in terms of year. The % will be included in plot 

max_year = df_singapore_condo_apt_data['Year'].value_counts()
df_2017 =df_year_remove.iloc[0]
df_2018 =df_year_remove.iloc[1]
df_2019 =df_year_remove.iloc[2]
df_2020 =df_year_remove.iloc[3]
df_2021 =df_year_remove.iloc[4]


tot_year_count = df_singapore_condo_apt_data['Project Name'].count()
percent_17 = round((df_2017/tot_year_count)*100,2)
percent_18 = round((df_2018/tot_year_count)*100,2)
percent_19 = round((df_2019/tot_year_count)*100,2)
percent_20 = round((df_2020/tot_year_count)*100,2)
percent_21 = round((df_2021/tot_year_count)*100,2)

print("Percentage of total number of units - 2017 is", percent_17,"%")
print("Percentage of total number of units - 2018 is", percent_18,"%")
print("Percentage of total number of units - 2019 is", percent_19,"%")
print("Percentage of total number of units - 2020 is", percent_20,"%")
print("Percentage of total number of units - 2021 is", percent_21,"%")

Percentage of total number of units - 2017 is 19.71 %
Percentage of total number of units - 2018 is 18.29 %
Percentage of total number of units - 2019 is 15.71 %
Percentage of total number of units - 2020 is 17.23 %
Percentage of total number of units - 2021 is 27.71 %

# Plotting Bar graph for Transactions For Number of Units - Year
#plt.style.use("Solarize_Light2")
#df_year_remove_plt = df_year_remove.plot(kind='bar',color = "orange", ec="grey", figsize=(20,10), title= "Transactions For Number of Units - Year")
#plt.xlabel('Year')
#plt.text(0,df_year_remove[0],'19.71%',ha='center',fontsize=15)
#plt.text(1,df_year_remove[1],'18.29%',ha='center',fontsize=15)
#plt.text(2,df_year_remove[2],'15.71%',ha='center',fontsize=15)
#plt.text(3,df_year_remove[3],'17.23%',ha='center',fontsize=15)
#plt.text(4,df_year_remove[4],'27.71%',ha='center',fontsize=15)

3. Trends and Analysis

Creating Parallel Coordinates Plots for Analysis

From the Parallel Coordinates Plot, we can observe that there is a high volume in the number of transactions within the number of units. And the highest Unit Price is $6210 per square feet.

import plotly.express as px
import pandas as pd
df_singapore_condo_apt_data

fig = px.parallel_coordinates(
    df_singapore_condo_apt_data,
    dimensions=[
        'Unit Price ($psf)', 'Price ($)','No. of Units', 'Postal District','Year', 
    ],
    color='Year',
    color_continuous_scale=px.colors.sequential.Emrld)
fig.show()

# Creating Parallel Coordinates Plot for Unit Price less than $3000
df_un_lesser = df_singapore_condo_apt_data[(df_singapore_condo_apt_data["Unit Price ($psf)"] < 3000 )]
df_un_lesser

	Project Name	Street Name	Type	Postal District	Market Segment	Tenure	Type of Sale	No. of Units	Price ($)	Nett Price ($)	Area (Sqft)	Type of Area	Floor Level	Unit Price ($psf)	Date of Sale	Year	Month
35356	ARDMORE PARK	ARDMORE PARK	Condominium	10	CCR	Freehold	Resale	1	8650000	-	2885	Strata	16 to 20	2999	2017-06-01	2017	6
21749	CANNINGHILL PIERS	CLARKE QUAY	Apartment	6	RCR	99 yrs lease commencing from 2021	New Sale	1	2841000	-	947	Strata	31 to 35	2999	2021-11-01	2021	11
29502	MIDTOWN BAY	BEACH ROAD	Apartment	7	CCR	99 yrs lease commencing from 2018	New Sale	1	1423400	1387400	463	Strata	16 to 20	2998	2019-10-01	2019	10
25939	THE ATELIER	MAKEWAY AVENUE	Apartment	9	CCR	Freehold	New Sale	1	4485000	-	1496	Strata	16 to 20	2998	2021-03-01	2021	3
25336	NOUVEL 18	ANDERSON ROAD	Condominium	10	CCR	Freehold	Resale	1	8456000	-	2820	Strata	16 to 20	2998	2021-03-01	2021	3
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
91278	MAYFAIR PARK	JALAN BINGKA	Apartment	21	RCR	99 yrs lease commencing from 1952	Resale	1	400000	-	915	Strata	01 to 05	437	2021-06-01	2021	6
60017	RESIDENTIAL APARTMENTS	BALESTIER ROAD	Apartment	12	RCR	9999 yrs lease commencing from 1961	Resale	1	820000	-	1900	Strata	01 to 05	432	2017-03-01	2017	3
46345	RESIDENTIAL APARTMENTS	JOO CHIAT TERRACE	Apartment	15	RCR	Freehold	Resale	1	505000	-	1195	Strata	01 to 05	423	2020-01-01	2020	1
35044	RESIDENTIAL APARTMENTS	SING JOO WALK	Apartment	8	RCR	Freehold	Resale	1	525000	-	1499	Strata	01 to 05	350	2017-07-01	2017	7
61963	BEDOK SHOPPING COMPLEX	BEDOK ROAD	Apartment	16	OCR	60 yrs lease commencing from 1977	Resale	1	340000	-	1216	Strata	01 to 05	280	2021-10-01	2021	10

103921 rows × 17 columns

#import plotly.express as px
#import pandas as pd
#df_un_lesser

#fig = px.parallel_coordinates(
    #df_un_lesser,
    #dimensions=[
        #'Unit Price ($psf)', 'Price ($)','No. of Units', 'Postal District','Year', 
    #],
    #color='Year',
    #color_continuous_scale=px.colors.sequential.Emrld)
#fig.show()

# Creating Parallel Coordinates Plot for Unit Price more than $3000
df_un_more = df_singapore_condo_apt_data[(df_singapore_condo_apt_data["Unit Price ($psf)"] > 3000 )]
df_un_more

	Project Name	Street Name	Type	Postal District	Market Segment	Tenure	Type of Sale	No. of Units	Price ($)	Nett Price ($)	Area (Sqft)	Type of Area	Floor Level	Unit Price ($psf)	Date of Sale	Year	Month
22566	LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	75000000	-	12077	Strata	01 to 05	6210	2021-10-01	2021	10
25439	EDEN	DRAYCOTT PARK	Apartment	10	CCR	Freehold	Resale	1	18287000	-	3035	Strata	21 to 25	6024	2021-03-01	2021	3
24587	LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	39000000	-	6577	Strata	01 to 05	5930	2021-05-01	2021	5
24549	PARK NOVA	TOMLINSON ROAD	Apartment	10	CCR	Freehold	New Sale	1	34438000	-	5899	Strata	16 to 20	5838	2021-05-01	2021	5
21278	LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	35000000	-	6049	Strata	01 to 05	5786	2021-12-01	2021	12
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
31967	NASSIM PARK RESIDENCES	NASSIM ROAD	Condominium	10	CCR	Freehold	Resale	1	20800000	-	6932	Strata	01 to 05	3001	2018-06-01	2018	6
22548	15 HOLLAND HILL	HOLLAND HILL	Apartment	10	CCR	Freehold	New Sale	1	5587900	-	1862	Strata	06 to 10	3001	2021-10-01	2021	10
35017	GRAMERCY PARK	GRANGE ROAD	Condominium	10	CCR	Freehold	Resale	1	3812000	-	1270	Strata	11 to 15	3001	2017-07-01	2017	7
28779	8 SAINT THOMAS	ST. THOMAS WALK	Condominium	9	CCR	Freehold	Resale	1	3908000	-	1302	Strata	11 to 15	3001	2020-01-01	2020	1
33034	NEW FUTURA	LEONIE HILL ROAD	Condominium	9	CCR	Freehold	Resale	1	8077000	-	2691	Strata	16 to 20	3001	2018-02-01	2018	2

2005 rows × 17 columns

#import plotly.express as px
#import pandas as pd
#df_un_more

#fig = px.parallel_coordinates(
    #df_un_more,
    #dimensions=[
        #'Unit Price ($psf)', 'Price ($)','No. of Units', 'Postal District','Year', 
    #],
    #color='Year',
    #color_continuous_scale=px.colors.sequential.Emrld)
#fig.show()

4. Using Unit Price ($psf) median for analysis

#Groupby the Unit Price and Number of Units
df_unit_v_no = df_singapore_condo_apt_data.groupby(['Unit Price ($psf)'])['No. of Units'].sum().reset_index()
df_unit_v_no.head(5)

	Unit Price ($psf)	No. of Units
0	280	1
1	350	1
2	423	1
3	432	1
4	437	1

# Sorting according to number of units for Unit Price($psf)
df_sort = df_unit_v_no.sort_values(["No. of Units"],axis=0,ascending=[False]).reset_index()
df_sort.head(5)

	index	Unit Price ($psf)	No. of Units
0	513	1024	618
1	825	1336	510
2	860	1371	438
3	605	1116	394
4	888	1399	388

# Finding the maximum value for both Unit price and number of units
df_unit_v_no.max()

Unit Price ($psf)    6210
No. of Units          618
dtype: int64

# df_unit_v_no dataset has 3325 rows and 2 columns
df_unit_v_no.shape

(3325, 2)

# Creating for analysis for mean and median
#sns.histplot(df_singapore_condo_apt_data['Unit Price ($psf)'],kde=False, bins=30)

Histogram Analysis

The graph indicates a Right Skewed histogram and the mean is more than median. Most of the units transacted are in the range of about 1000 to 2000 dollars per square feet. Thus, for our analysis in the dataset, using median might be more suitable for our analysis.

5. Unit Price($psf) Analysis - (mean and median)

# Unit Price Analysis - Jan 2017 to Jan 2022 (mean and median)
total_mean_u  = df_singapore_condo_apt_data['Unit Price ($psf)'].mean(axis=0)
total_median_u  = df_singapore_condo_apt_data['Unit Price ($psf)'].median(axis=0)

print("The mean Unit Price ($psf) of property in all districts is:$", round(total_mean_u,2)) # mean
print("The median Unit Price ($psf) of property in all districts is:$", round(total_median_u,2)) # median

The mean Unit Price ($psf) of property in all districts is:$ 1562.79
The median Unit Price ($psf) of property in all districts is:$ 1491.0

# Comparing the median Unit Price ($psf) from 2017 to 2021
y17 = 2017
y18 = 2018
y19 = 2019
y20 = 2020
y21 = 2021

year2017 = df_singapore_condo_apt_data[(df_singapore_condo_apt_data["Year"] ==2017 )]
year2018 = df_singapore_condo_apt_data[(df_singapore_condo_apt_data["Year"] ==2018 )]
year2019 = df_singapore_condo_apt_data[(df_singapore_condo_apt_data["Year"] ==2019 )]
year2020 = df_singapore_condo_apt_data[(df_singapore_condo_apt_data["Year"] ==2020 )]
year2021 = df_singapore_condo_apt_data[(df_singapore_condo_apt_data["Year"] ==2021 )]


median_17 = year2017['Unit Price ($psf)'].median()
median_18 = year2018['Unit Price ($psf)'].median()
median_19 = year2019['Unit Price ($psf)'].median()
median_20 = year2020['Unit Price ($psf)'].median()
median_21 = year2021['Unit Price ($psf)'].median()

print("The median price in all districts is $", round(total_median_u,2)) # median
print("The median price for the year", y17, "is $", round(median_17,2) )
print("The median price for the year", y18, "is $", round(median_18,2) )
print("The median price for the year", y19, "is $", round(median_19,2) )
print("The median price for the year", y20, "is $", round(median_20,2) )
print("The median price for the year", y21, "is $", round(median_21,2) )

The median price in all districts is $ 1491.0
The median price for the year 2017 is $ 1346.0
The median price for the year 2018 is $ 1418.0
The median price for the year 2019 is $ 1515.0
The median price for the year 2020 is $ 1552.0
The median price for the year 2021 is $ 1610.0

# Comparing the mean Unit Price ($psf) from 2017 to 2021
y17 = 2017
y18 = 2018
y19 = 2019
y20 = 2020
y21 = 2021

total_median_price  = df_singapore_condo_apt_data['Price ($)'].median(axis=0)
year2017 = df_singapore_condo_apt_data[(df_singapore_condo_apt_data["Year"] ==2017 )]
year2018 = df_singapore_condo_apt_data[(df_singapore_condo_apt_data["Year"] ==2018 )]
year2019 = df_singapore_condo_apt_data[(df_singapore_condo_apt_data["Year"] ==2019 )]
year2020 = df_singapore_condo_apt_data[(df_singapore_condo_apt_data["Year"] ==2020 )]
year2021 = df_singapore_condo_apt_data[(df_singapore_condo_apt_data["Year"] ==2021 )]

mean_17 = year2017['Unit Price ($psf)'].mean()
mean_18 = year2018['Unit Price ($psf)'].mean()
mean_19 = year2019['Unit Price ($psf)'].mean()
mean_20 = year2020['Unit Price ($psf)'].mean()
mean_21 = year2021['Unit Price ($psf)'].mean()

print("The median Unit Price ($psf) of property in all districts is:$", round(total_mean_u,2)) # median
print("The median Unit Price ($psf) for the year", y17, "is $", round(mean_17,2) )
print("The median Unit Price ($psf) for the year", y18, "is $", round(mean_18,2) )
print("The median Unit Price ($psf) for the year", y19, "is $", round(mean_19,2) )
print("The median Unit Price ($psf) for the year", y20, "is $", round(mean_20,2) )
print("The median Unit Price ($psf) for the year", y21, "is $", round(mean_21,2) )

The median Unit Price ($psf) of property in all districts is:$ 1562.79
The median Unit Price ($psf) for the year 2017 is $ 1404.46
The median Unit Price ($psf) for the year 2018 is $ 1486.61
The median Unit Price ($psf) for the year 2019 is $ 1606.84
The median Unit Price ($psf) for the year 2020 is $ 1577.3
The median Unit Price ($psf) for the year 2021 is $ 1681.45

#Creating table for mean median price comparison
import pandas as pd
from tabulate import tabulate

# creating a DataFrame
dict_district = {'Year' : ['2017 ', '2018', '2019', '2020', "2021"],
        'Mean Price ($)' : [mean_17, mean_18, mean_19,mean_20,mean_21 ],
        'Median Price ($)' : [median_17, median_18, median_19,median_20,median_21 ]
        
       }
dataframe3 = pd.DataFrame(dict_district)

  
# displaying the DataFrame
dataframe3
dataframe3.style.background_gradient()

#dataframe1.style.highlight_max(color = 'lightgreen', axis = 0)
#print(tabulate(dataframe1, headers = 'keys', tablefmt = 'pretty'))

	Year	Mean Price ($)	Median Price ($)
0	2017	1404.460374	1346.000000
1	2018	1486.608258	1418.000000
2	2019	1606.844268	1515.000000
3	2020	1577.298461	1552.000000
4	2021	1681.446409	1610.000000

#Plotting mean and median for Unit Price ($psf)
dataframe3.plot(x = "Year",figsize=(20,10),  rot = 90, title = 'Unit Price($psf) Analysis - Jan 2017 to Jan 2022 (mean and median)')
plt.ylabel('Price ($)')

Text(0, 0.5, 'Price ($)')

Unit Price($psf) Analysis - (mean and median)

• The graph shows that price for both mean and median are falling each other and there are no clear outliers. The median and mean are on upward trend. The mean price went down in 2020 but 2021 has the highest mean and median prices.

# Creating boxplot to see trend for median
sns.boxplot(x="Year", y="Unit Price ($psf)", data=df_singapore_condo_apt_data, palette="coolwarm")

<AxesSubplot:xlabel='Year', ylabel='Unit Price ($psf)'>

2017 to 2022 - Unit Price ($psf) observation using plotly for data visualization.

# Plotly to analyze the Unit Price ($psf) - 2017 to 2022 dataset
#trace0_1_overall = go.Box(y = df_singapore_condo_apt_data['Unit Price ($psf)'], 
                #name = 'Unit Price ($psf)',
               #marker_color = 'lightseagreen')

#data = [trace0_1_overall]

#offline.iplot(data)

# Use Plotly to analyze the Unit Price ($psf) - 2017 to 2022 dataset
#trace0_2_overall = go.Box(y = df_singapore_condo_apt_data['Unit Price ($psf)'], 
                #name = 'Unit Price ($psf)', marker_color = 'lightseagreen',
                
                #boxpoints = 'all',  # see the actual points which make up our data for the box plot
                
                #boxmean = True  # draw dotted line for the mean                 
               #)

#data = [trace0_2_overall]

#offline.iplot(data)

2017 - Use Plotly to analyze the Unit Price ($psf)

• https://plotly.com/~ab.rauf/41/

• https://plotly.com/~ab.rauf/43/

2018 - Use Plotly to analyze the Unit Price ($psf)

• https://plotly.com/~ab.rauf/24/

2019

• https://plotly.com/~ab.rauf/28/

• https://plotly.com/~ab.rauf/30/

2020

• https://plotly.com/~ab.rauf/33/

• https://plotly.com/~ab.rauf/35/

2021

• https://plotly.com/~ab.rauf/37/

• https://plotly.com/~ab.rauf/39/

Use Plotly to analyze and compare the Unit Price ($psf) through the 5 years with outliers

# Use Plotly to analyze and compare the Unit Price ($psf) through the 5 years with outliers
#data = [trace0_1_overall,trace1_1_2017, trace2_1_2018, trace3_1_2019, trace4_1_2020, trace5_1_2021]

#offline.iplot(data)

#data = [trace0_2_overall,trace1_2_2017, trace2_2_2018, trace3_2_2019, trace4_2_2020, trace5_2_2021]

#offline.iplot(data)

Study the trend and find the % change in median Unit Price ($psf) for frequency range of 3 months.

#Resampling for a frequency of 3 months
grouper = df_singapore_condo_apt_data.groupby(pd.Grouper(key="Date of Sale",  freq="3M"))
grouper

<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000018D0FC06D90>

grouper.head()

	Project Name	Street Name	Type	Postal District	Market Segment	Tenure	Type of Sale	No. of Units	Price ($)	Nett Price ($)	Area (Sqft)	Type of Area	Floor Level	Unit Price ($psf)	Date of Sale	Year	Month
36562	ARDMORE THREE	ARDMORE PARK	Condominium	10	CCR	Freehold	Resale	1	6543555	-	1787	Strata	26 to 30	3662	2017-02-01	2017	2
36658	ARDMORE PARK	ARDMORE PARK	Condominium	10	CCR	Freehold	Resale	1	9500000	-	2885	Strata	21 to 25	3293	2017-02-01	2017	2
36597	OUE TWIN PEAKS	LEONIE HILL ROAD	Condominium	9	CCR	99 yrs lease commencing from 2010	Resale	1	1760100	-	549	Strata	31 to 35	3206	2017-02-01	2017	2
36567	OUE TWIN PEAKS	LEONIE HILL ROAD	Condominium	9	CCR	99 yrs lease commencing from 2010	Resale	1	1725500	-	549	Strata	26 to 30	3143	2017-02-01	2017	2
36609	OUE TWIN PEAKS	LEONIE HILL ROAD	Condominium	9	CCR	99 yrs lease commencing from 2010	Resale	1	1791600	-	570	Strata	26 to 30	3140	2017-02-01	2017	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
21278	LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	35000000	-	6049	Strata	01 to 05	5786	2021-12-01	2021	12
21345	LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	37000000	-	6523	Strata	01 to 05	5672	2021-12-01	2021	12
21330	PARK NOVA	TOMLINSON ROAD	Apartment	10	CCR	Freehold	New Sale	1	10479000	-	2207	Strata	06 to 10	4749	2021-12-01	2021	12
21241	TWENTYONE ANGULLIA PARK	ANGULLIA PARK	Condominium	9	CCR	Freehold	Resale	1	10138000	-	2260	Strata	31 to 35	4485	2021-12-01	2021	12
21496	KLIMT CAIRNHILL	CAIRNHILL ROAD	Apartment	9	CCR	Freehold	New Sale	1	6000000	-	1496	Strata	26 to 30	4010	2021-12-01	2021	12

105 rows × 17 columns

Median Resampling - Unit Price ($psf)

med_gp = grouper.median() #median value
med_gp

	Postal District	No. of Units	Price ($)	Area (Sqft)	Unit Price ($psf)	Year	Month
Date of Sale
2017-02-28	10.0	1.0	1164240.0	980.0	1311.0	2017.0	2.0
2017-05-31	15.0	1.0	1229999.0	958.0	1385.0	2017.0	4.0
2017-08-31	14.0	1.0	1275060.0	1023.0	1332.5	2017.0	7.0
2017-11-30	15.0	1.0	1290000.0	1033.0	1334.0	2017.0	10.0
2018-02-28	15.0	1.0	1312000.0	1087.0	1315.0	2018.0	2.0
2018-05-31	15.0	1.0	1268888.0	990.0	1390.0	2018.0	4.0
2018-08-31	14.0	1.0	1210000.0	893.0	1449.0	2018.0	7.0
2018-11-30	15.0	1.0	1254000.0	904.0	1526.5	2018.0	10.0
2019-02-28	14.0	1.0	1280000.0	947.0	1460.0	2019.0	2.0
2019-05-31	15.0	1.0	1270000.0	915.0	1469.0	2019.0	4.0
2019-08-31	14.0	1.0	1286000.0	883.0	1504.0	2019.0	7.0
2019-11-30	14.0	1.0	1319000.0	883.0	1602.0	2019.0	10.0
2020-02-29	14.0	1.0	1337400.0	904.0	1592.0	2020.0	2.0
2020-05-31	15.0	1.0	1266950.0	904.0	1513.0	2020.0	3.0
2020-08-31	16.0	1.0	1328000.0	915.0	1571.0	2020.0	7.0
2020-11-30	15.0	1.0	1350000.0	958.0	1517.0	2020.0	10.0
2021-02-28	14.0	1.0	1340000.0	915.0	1615.0	2021.0	2.0
2021-05-31	14.0	1.0	1463000.0	958.0	1605.0	2021.0	4.0
2021-08-31	16.0	1.0	1390150.0	947.0	1578.0	2021.0	7.0
2021-11-30	14.0	1.0	1530000.0	947.0	1633.0	2021.0	10.0
2022-02-28	15.0	1.0	1560000.0	980.0	1659.0	2021.0	12.0

med_gp = med_gp.drop(med_gp.index[20]) #drop 2022 as our analysis is from 2017 to 2021 for median resampling
med_gp

	Postal District	No. of Units	Price ($)	Area (Sqft)	Unit Price ($psf)	Year	Month
Date of Sale
2017-02-28	10.0	1.0	1164240.0	980.0	1311.0	2017.0	2.0
2017-05-31	15.0	1.0	1229999.0	958.0	1385.0	2017.0	4.0
2017-08-31	14.0	1.0	1275060.0	1023.0	1332.5	2017.0	7.0
2017-11-30	15.0	1.0	1290000.0	1033.0	1334.0	2017.0	10.0
2018-02-28	15.0	1.0	1312000.0	1087.0	1315.0	2018.0	2.0
2018-05-31	15.0	1.0	1268888.0	990.0	1390.0	2018.0	4.0
2018-08-31	14.0	1.0	1210000.0	893.0	1449.0	2018.0	7.0
2018-11-30	15.0	1.0	1254000.0	904.0	1526.5	2018.0	10.0
2019-02-28	14.0	1.0	1280000.0	947.0	1460.0	2019.0	2.0
2019-05-31	15.0	1.0	1270000.0	915.0	1469.0	2019.0	4.0
2019-08-31	14.0	1.0	1286000.0	883.0	1504.0	2019.0	7.0
2019-11-30	14.0	1.0	1319000.0	883.0	1602.0	2019.0	10.0
2020-02-29	14.0	1.0	1337400.0	904.0	1592.0	2020.0	2.0
2020-05-31	15.0	1.0	1266950.0	904.0	1513.0	2020.0	3.0
2020-08-31	16.0	1.0	1328000.0	915.0	1571.0	2020.0	7.0
2020-11-30	15.0	1.0	1350000.0	958.0	1517.0	2020.0	10.0
2021-02-28	14.0	1.0	1340000.0	915.0	1615.0	2021.0	2.0
2021-05-31	14.0	1.0	1463000.0	958.0	1605.0	2021.0	4.0
2021-08-31	16.0	1.0	1390150.0	947.0	1578.0	2021.0	7.0
2021-11-30	14.0	1.0	1530000.0	947.0	1633.0	2021.0	10.0

med_gp.reset_index()
med_gp.head()

	Postal District	No. of Units	Price ($)	Area (Sqft)	Unit Price ($psf)	Year	Month
Date of Sale
2017-02-28	10.0	1.0	1164240.0	980.0	1311.0	2017.0	2.0
2017-05-31	15.0	1.0	1229999.0	958.0	1385.0	2017.0	4.0
2017-08-31	14.0	1.0	1275060.0	1023.0	1332.5	2017.0	7.0
2017-11-30	15.0	1.0	1290000.0	1033.0	1334.0	2017.0	10.0
2018-02-28	15.0	1.0	1312000.0	1087.0	1315.0	2018.0	2.0

med_gp.index = ['Q1-2017', ' Q2-2017', 'Q3-2017', 'Q4-2017',
                'Q1-2018', ' Q2-2018', 'Q3-2018', 'Q4-2018', 
                'Q1-2019', ' Q2-2019', 'Q3-2019', 'Q4-2019',
                'Q1-2020', ' Q2-2020', 'Q3-2020', 'Q4-2020',
                'Q1-2021', ' Q2-2021', 'Q3-2021', 'Q4-2021',]
med_gp.head()

	Postal District	No. of Units	Price ($)	Area (Sqft)	Unit Price ($psf)	Year	Month
Q1-2017	10.0	1.0	1164240.0	980.0	1311.0	2017.0	2.0
Q2-2017	15.0	1.0	1229999.0	958.0	1385.0	2017.0	4.0
Q3-2017	14.0	1.0	1275060.0	1023.0	1332.5	2017.0	7.0
Q4-2017	15.0	1.0	1290000.0	1033.0	1334.0	2017.0	10.0
Q1-2018	15.0	1.0	1312000.0	1087.0	1315.0	2018.0	2.0

# Plot Median Unit Price ($psf) bar graph
med_gp.plot(kind='bar', y = 'Unit Price ($psf)', color = "skyblue", ec="grey", figsize=(20,10), rot =30, title= "Median Unit Price ($psf) 2017 to 2022")
plt.xlabel('Year')
plt.ylabel('Unit Price ($psf)')
plt.show()

# Percent Change - median
med_p_change = med_gp.pct_change()
med_p_change.head()

	Postal District	No. of Units	Price ($)	Area (Sqft)	Unit Price ($psf)	Year	Month
Q1-2017	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Q2-2017	0.500000	0.0	0.056482	-0.022449	0.056445	0.000000	1.000000
Q3-2017	-0.066667	0.0	0.036635	0.067850	-0.037906	0.000000	0.750000
Q4-2017	0.071429	0.0	0.011717	0.009775	0.001126	0.000000	0.428571
Q1-2018	0.000000	0.0	0.017054	0.052275	-0.014243	0.000496	-0.800000

# converting percentage change
convert_perc = med_p_change['Unit Price ($psf)']*100
convert_perc.head()

Q1-2017          NaN
 Q2-2017    5.644546
Q3-2017    -3.790614
Q4-2017     0.112570
Q1-2018    -1.424288
Name: Unit Price ($psf), dtype: float64

convert_perc.plot(kind='bar', y = "Unit Price ($psf)",figsize=(20,10),  title = "Percentage Change - Median Price (January 2017 to January 2022)")
plt.xlabel('Year')
plt.ylabel('Percentage Change')
plt.show()

Analysis for Area(sqft) for year 2020 and 2021

# dataset for 2020
df_2020 = df_singapore_condo_apt_data[(df_singapore_condo_apt_data["Year"] ==2020)]
df_2020

	Project Name	Street Name	Type	Postal District	Market Segment	Tenure	Type of Sale	No. of Units	Price ($)	Nett Price ($)	Area (Sqft)	Type of Area	Floor Level	Unit Price ($psf)	Date of Sale	Year	Month
9399	WALLICH RESIDENCE	WALLICH STREET	Apartment	2	CCR	99 yrs lease commencing from 2011	Resale	1	17500000	-	3509	Strata	61 to 65	4987	2020-01-01	2020	1
27031	SKYLINE @ ORCHARD BOULEVARD	ANGULLIA PARK	Apartment	9	CCR	Freehold	Resale	1	17650000	-	3767	Strata	26 to 30	4685	2020-10-01	2020	10
28771	LE NOUVEL ARDMORE	ARDMORE PARK	Condominium	10	CCR	Freehold	Resale	1	17200000	-	3843	Strata	26 to 30	4476	2020-01-01	2020	1
28790	LE NOUVEL ARDMORE	ARDMORE PARK	Condominium	10	CCR	Freehold	Resale	1	18213450	-	4187	Strata	11 to 15	4350	2020-01-01	2020	1
26788	LE NOUVEL ARDMORE	ARDMORE PARK	Condominium	10	CCR	Freehold	Resale	1	17800000	-	4133	Strata	21 to 25	4306	2020-11-01	2020	11
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
70164	LIGHTHOUSE	JALAN LOYANG BESAR	Condominium	17	OCR	99 yrs lease commencing from 1993	Resale	1	1570000	-	3079	Strata	01 to 05	510	2020-09-01	2020	9
102393	EUPHONY GARDENS	JALAN MATA AYER	Condominium	27	OCR	99 yrs lease commencing from 1998	Resale	1	1100000	-	2196	Strata	01 to 05	501	2020-12-01	2020	12
71269	RESIDENTIAL APARTMENTS	PILLAI ROAD	Apartment	19	OCR	Freehold	Resale	1	928000	-	1898	Land	01 to 05	489	2020-07-01	2020	7
69454	EASTWOOD CENTRE	EASTWOOD ROAD	Apartment	16	OCR	99 yrs lease commencing from 1995	Resale	1	1500000	-	3197	Strata	01 to 05	469	2020-10-01	2020	10
46345	RESIDENTIAL APARTMENTS	JOO CHIAT TERRACE	Apartment	15	RCR	Freehold	Resale	1	505000	-	1195	Strata	01 to 05	423	2020-01-01	2020	1

18257 rows × 17 columns

# Find the size vs units (2021)
df_size_2020 = df_2020.groupby(['Area (Sqft)'])['No. of Units'].sum().reset_index()
df_size_2020.head()

	Area (Sqft)	No. of Units
0	323	1
1	334	2
2	344	6
3	355	6
4	366	15

df_sort_20 = df_size_2020.sort_values(["No. of Units"],axis=0,ascending=[False])
df_sort_20.head()

	Area (Sqft)	No. of Units
41	764	523
33	678	497
19	527	416
34	689	334
60	958	323

# Plot graph for 2020 (Area (Sqft))
df_sort_20[:3].plot(kind='bar', x = 'Area (Sqft)', y = 'No. of Units', color = "skyblue", ec="grey", rot = 360,fontsize = 18,figsize=(20,10), title= "Number of Transactions (Area (Sqft) - 2020")
plt.xlabel('Area (Sqft)')
plt.ylabel('No. of Units')
plt.show()

df_2021 = df_singapore_condo_apt_data[(df_singapore_condo_apt_data["Year"] ==2021)]
df_2021

	Project Name	Street Name	Type	Postal District	Market Segment	Tenure	Type of Sale	No. of Units	Price ($)	Nett Price ($)	Area (Sqft)	Type of Area	Floor Level	Unit Price ($psf)	Date of Sale	Year	Month
22566	LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	75000000	-	12077	Strata	01 to 05	6210	2021-10-01	2021	10
25439	EDEN	DRAYCOTT PARK	Apartment	10	CCR	Freehold	Resale	1	18287000	-	3035	Strata	21 to 25	6024	2021-03-01	2021	3
24587	LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	39000000	-	6577	Strata	01 to 05	5930	2021-05-01	2021	5
24549	PARK NOVA	TOMLINSON ROAD	Apartment	10	CCR	Freehold	New Sale	1	34438000	-	5899	Strata	16 to 20	5838	2021-05-01	2021	5
21278	LES MAISONS NASSIM	NASSIM ROAD	Condominium	10	CCR	Freehold	New Sale	1	35000000	-	6049	Strata	01 to 05	5786	2021-12-01	2021	12
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
65552	EASTWOOD CENTRE	EASTWOOD ROAD	Apartment	16	OCR	99 yrs lease commencing from 1995	Resale	1	1580000	-	2992	Strata	01 to 05	528	2021-05-01	2021	5
91277	LAKESIDE TOWER	YUAN CHING ROAD	Apartment	22	OCR	99 yrs lease commencing from 1975	Resale	1	2038000	-	3875	Strata	16 to 20	526	2021-06-01	2021	6
101344	TEACHER'S HOUSING ESTATE	KALIDASA AVENUE	Apartment	26	OCR	999 yrs lease commencing from 1885	Resale	1	1000000	-	2196	Strata	01 to 05	455	2021-09-01	2021	9
91278	MAYFAIR PARK	JALAN BINGKA	Apartment	21	RCR	99 yrs lease commencing from 1952	Resale	1	400000	-	915	Strata	01 to 05	437	2021-06-01	2021	6
61963	BEDOK SHOPPING COMPLEX	BEDOK ROAD	Apartment	16	OCR	60 yrs lease commencing from 1977	Resale	1	340000	-	1216	Strata	01 to 05	280	2021-10-01	2021	10

29352 rows × 17 columns

# Find the size vs units (2021)
df_size_21 = df_2021.groupby(['Area (Sqft)'])['No. of Units'].sum().reset_index()
df_size_21.head()

	Area (Sqft)	No. of Units
0	323	5
1	334	12
2	344	11
3	355	21
4	366	21

df_sort_21 = df_size_21.sort_values(["No. of Units"],axis=0,ascending=[False])
df_sort_21.head()

	Area (Sqft)	No. of Units
57	936	546
31	657	480
38	732	455
55	915	455
36	710	444

# Mean price in district
df_sort_21[:3].plot(kind='bar', x = 'Area (Sqft)', y = 'No. of Units', color = "lightgreen", ec="grey", rot = 360,figsize=(20,10), fontsize = 18,title= "Number of Transactions (Area (Sqft) - 2021")
plt.xlabel('Area (Sqft)')
plt.ylabel('No. of Units')

plt.show()

Analysis for homes for smaller units and Family Unit of 4

# Analysis for smaller unit homes
df_smaller = df_singapore_condo_apt_data[((df_singapore_condo_apt_data['Price ($)'].between(600000,850000, inclusive = True) & 
                              (df_singapore_condo_apt_data['Unit Price ($psf)'] ==1610) &
                              (df_singapore_condo_apt_data['Area (Sqft)'].between(400,750, inclusive = True))))]
df_smaller

	Project Name	Street Name	Type	Postal District	Market Segment	Tenure	Type of Sale	No. of Units	Price ($)	Nett Price ($)	Area (Sqft)	Type of Area	Floor Level	Unit Price ($psf)	Date of Sale	Year	Month
88147	THOMSON IMPRESSIONS	LORONG PUNTONG	Apartment	20	RCR	99 yrs lease commencing from 2015	New Sale	1	745000	-	463	Strata	16 to 20	1610	2017-03-01	2017	3
50603	CENTRA HEIGHTS	SIMS AVENUE	Apartment	14	RCR	Freehold	Resale	1	693388	-	431	Strata	11 to 15	1610	2018-11-01	2018	11
37954	PRESTIGE HEIGHTS	BALESTIER ROAD	Apartment	12	RCR	Freehold	Resale	1	780000	-	484	Strata	06 to 10	1610	2021-10-01	2021	10
46735	THE TRE VER	POTONG PASIR AVENUE 1	Condominium	13	RCR	99 yrs lease commencing from 2018	New Sale	1	797000	-	495	Strata	11 to 15	1610	2019-11-01	2019	11
11033	PARC CLEMATIS	JALAN LEMPENG	Apartment	5	OCR	99 yrs lease commencing from 2019	New Sale	1	832000	-	517	Strata	06 to 10	1610	2019-08-01	2019	8
62734	THOMSON IMPRESSIONS	LORONG PUNTONG	Apartment	20	RCR	99 yrs lease commencing from 2015	Resale	1	745000	-	463	Strata	06 to 10	1610	2021-09-01	2021	9
72749	AFFINITY AT SERANGOON	SERANGOON NORTH AVENUE 1	Apartment	19	OCR	99 yrs lease commencing from 2018	New Sale	1	780000	-	484	Strata	01 to 05	1610	2020-03-01	2020	3
6286	CLAVON	CLEMENTI AVENUE 1	Apartment	5	OCR	99 yrs lease commencing from 2019	New Sale	1	849000	-	527	Strata	06 to 10	1610	2020-12-01	2020	12
41950	WATERBANK AT DAKOTA	DAKOTA CRESCENT	Condominium	14	RCR	99 yrs lease commencing from 2009	Resale	1	780000	-	484	Strata	01 to 05	1610	2021-02-01	2021	2
49589	THE TRE VER	POTONG PASIR AVENUE 1	Condominium	13	RCR	99 yrs lease commencing from 2018	New Sale	1	797000	-	495	Strata	16 to 20	1610	2019-03-01	2019	3
55989	REZI 35	LORONG 35 GEYLANG	Apartment	14	RCR	Freehold	New Sale	1	727720	-	452	Strata	01 to 05	1610	2017-11-01	2017	11
78300	CASA AL MARE	JALAN LOYANG BESAR	Apartment	17	OCR	Freehold	New Sale	1	780000	-	484	Strata	01 to 05	1610	2019-01-01	2019	1
99125	HILLION RESIDENCES	JELEBU ROAD	Apartment	23	OCR	99 yrs lease commencing from 2013	New Sale	1	745000	-	463	Strata	16 to 20	1610	2017-10-01	2017	10
37129	WATERBANK AT DAKOTA	DAKOTA CRESCENT	Condominium	14	RCR	99 yrs lease commencing from 2009	Resale	1	780000	-	484	Strata	06 to 10	1610	2021-12-01	2021	12
87725	THOMSON IMPRESSIONS	LORONG PUNTONG	Apartment	20	RCR	99 yrs lease commencing from 2015	New Sale	1	745000	-	463	Strata	06 to 10	1610	2017-04-01	2017	4
87328	STARS OF KOVAN	UPPER SERANGOON ROAD	Apartment	19	OCR	99 yrs lease commencing from 2015	New Sale	1	814420	-	506	Strata	16 to 20	1610	2017-05-01	2017	5
6563	CLAVON	CLEMENTI AVENUE 1	Apartment	5	OCR	99 yrs lease commencing from 2019	New Sale	1	849000	-	527	Strata	06 to 10	1610	2020-12-01	2020	12
54531	38 I SUITES	IPOH LANE	Apartment	15	RCR	Freehold	Resale	1	780000	-	484	Strata	11 to 15	1610	2018-03-01	2018	3
52884	BARTLEY RIDGE	MOUNT VERNON ROAD	Condominium	13	RCR	99 yrs lease commencing from 2012	Resale	1	745000	-	463	Strata	06 to 10	1610	2018-06-01	2018	6
7498	WHISTLER GRAND	WEST COAST VALE	Apartment	5	OCR	99 yrs lease commencing from 2018	New Sale	1	814400	-	506	Strata	26 to 30	1610	2020-09-01	2020	9

# Condominium for smaller homes
df_smaller_condominium = df_smaller[df_smaller['Type'].str.match('Condominium')] 
df_smaller_condominium

	Project Name	Street Name	Type	Postal District	Market Segment	Tenure	Type of Sale	No. of Units	Price ($)	Nett Price ($)	Area (Sqft)	Type of Area	Floor Level	Unit Price ($psf)	Date of Sale	Year	Month
46735	THE TRE VER	POTONG PASIR AVENUE 1	Condominium	13	RCR	99 yrs lease commencing from 2018	New Sale	1	797000	-	495	Strata	11 to 15	1610	2019-11-01	2019	11
41950	WATERBANK AT DAKOTA	DAKOTA CRESCENT	Condominium	14	RCR	99 yrs lease commencing from 2009	Resale	1	780000	-	484	Strata	01 to 05	1610	2021-02-01	2021	2
49589	THE TRE VER	POTONG PASIR AVENUE 1	Condominium	13	RCR	99 yrs lease commencing from 2018	New Sale	1	797000	-	495	Strata	16 to 20	1610	2019-03-01	2019	3
37129	WATERBANK AT DAKOTA	DAKOTA CRESCENT	Condominium	14	RCR	99 yrs lease commencing from 2009	Resale	1	780000	-	484	Strata	06 to 10	1610	2021-12-01	2021	12
52884	BARTLEY RIDGE	MOUNT VERNON ROAD	Condominium	13	RCR	99 yrs lease commencing from 2012	Resale	1	745000	-	463	Strata	06 to 10	1610	2018-06-01	2018	6

import pandas as pd
from tabulate import tabulate

# creating a DataFrame for Condominiums(smaller units)

dataframe_condo_s = pd.DataFrame(df_smaller_condominium)

  
# displaying the DataFrame
dataframe_condo_s
dataframe_condo_s.style.background_gradient()

dataframe_condo_s.style.highlight_max(color = 'lightgreen', axis = 0)
#print(tabulate(dataframe_condo, headers = 'keys', tablefmt = 'pretty'))

	Project Name	Street Name	Type	Postal District	Market Segment	Tenure	Type of Sale	No. of Units	Price ($)	Nett Price ($)	Area (Sqft)	Type of Area	Floor Level	Unit Price ($psf)	Date of Sale	Year	Month
46735	THE TRE VER	POTONG PASIR AVENUE 1	Condominium	13	RCR	99 yrs lease commencing from 2018	New Sale	1	797000	-	495	Strata	11 to 15	1610	2019-11-01 00:00:00	2019	11
41950	WATERBANK AT DAKOTA	DAKOTA CRESCENT	Condominium	14	RCR	99 yrs lease commencing from 2009	Resale	1	780000	-	484	Strata	01 to 05	1610	2021-02-01 00:00:00	2021	2
49589	THE TRE VER	POTONG PASIR AVENUE 1	Condominium	13	RCR	99 yrs lease commencing from 2018	New Sale	1	797000	-	495	Strata	16 to 20	1610	2019-03-01 00:00:00	2019	3
37129	WATERBANK AT DAKOTA	DAKOTA CRESCENT	Condominium	14	RCR	99 yrs lease commencing from 2009	Resale	1	780000	-	484	Strata	06 to 10	1610	2021-12-01 00:00:00	2021	12
52884	BARTLEY RIDGE	MOUNT VERNON ROAD	Condominium	13	RCR	99 yrs lease commencing from 2012	Resale	1	745000	-	463	Strata	06 to 10	1610	2018-06-01 00:00:00	2018	6

# Plotting the bar graph - Condominiums (Smaller Home)
dataframe_condo_s.plot(kind='bar', x = 'Project Name', y = 'Price ($)', color = "skyblue", ec="grey", 
                     rot =30, fontsize = 16,figsize=(20,10), title = "Condominiums - Smaller Homes" )
plt.xlabel('Project Name')
plt.ylabel('Price ($)')
plt.show()

# Apartment for smaller homes
df_smaller_apt = df_smaller[df_smaller['Type'].str.match('Apartment')] 
df_smaller_apt

	Project Name	Street Name	Type	Postal District	Market Segment	Tenure	Type of Sale	No. of Units	Price ($)	Nett Price ($)	Area (Sqft)	Type of Area	Floor Level	Unit Price ($psf)	Date of Sale	Year	Month
88147	THOMSON IMPRESSIONS	LORONG PUNTONG	Apartment	20	RCR	99 yrs lease commencing from 2015	New Sale	1	745000	-	463	Strata	16 to 20	1610	2017-03-01	2017	3
50603	CENTRA HEIGHTS	SIMS AVENUE	Apartment	14	RCR	Freehold	Resale	1	693388	-	431	Strata	11 to 15	1610	2018-11-01	2018	11
37954	PRESTIGE HEIGHTS	BALESTIER ROAD	Apartment	12	RCR	Freehold	Resale	1	780000	-	484	Strata	06 to 10	1610	2021-10-01	2021	10
11033	PARC CLEMATIS	JALAN LEMPENG	Apartment	5	OCR	99 yrs lease commencing from 2019	New Sale	1	832000	-	517	Strata	06 to 10	1610	2019-08-01	2019	8
62734	THOMSON IMPRESSIONS	LORONG PUNTONG	Apartment	20	RCR	99 yrs lease commencing from 2015	Resale	1	745000	-	463	Strata	06 to 10	1610	2021-09-01	2021	9
72749	AFFINITY AT SERANGOON	SERANGOON NORTH AVENUE 1	Apartment	19	OCR	99 yrs lease commencing from 2018	New Sale	1	780000	-	484	Strata	01 to 05	1610	2020-03-01	2020	3
6286	CLAVON	CLEMENTI AVENUE 1	Apartment	5	OCR	99 yrs lease commencing from 2019	New Sale	1	849000	-	527	Strata	06 to 10	1610	2020-12-01	2020	12
55989	REZI 35	LORONG 35 GEYLANG	Apartment	14	RCR	Freehold	New Sale	1	727720	-	452	Strata	01 to 05	1610	2017-11-01	2017	11
78300	CASA AL MARE	JALAN LOYANG BESAR	Apartment	17	OCR	Freehold	New Sale	1	780000	-	484	Strata	01 to 05	1610	2019-01-01	2019	1
99125	HILLION RESIDENCES	JELEBU ROAD	Apartment	23	OCR	99 yrs lease commencing from 2013	New Sale	1	745000	-	463	Strata	16 to 20	1610	2017-10-01	2017	10
87725	THOMSON IMPRESSIONS	LORONG PUNTONG	Apartment	20	RCR	99 yrs lease commencing from 2015	New Sale	1	745000	-	463	Strata	06 to 10	1610	2017-04-01	2017	4
87328	STARS OF KOVAN	UPPER SERANGOON ROAD	Apartment	19	OCR	99 yrs lease commencing from 2015	New Sale	1	814420	-	506	Strata	16 to 20	1610	2017-05-01	2017	5
6563	CLAVON	CLEMENTI AVENUE 1	Apartment	5	OCR	99 yrs lease commencing from 2019	New Sale	1	849000	-	527	Strata	06 to 10	1610	2020-12-01	2020	12
54531	38 I SUITES	IPOH LANE	Apartment	15	RCR	Freehold	Resale	1	780000	-	484	Strata	11 to 15	1610	2018-03-01	2018	3
7498	WHISTLER GRAND	WEST COAST VALE	Apartment	5	OCR	99 yrs lease commencing from 2018	New Sale	1	814400	-	506	Strata	26 to 30	1610	2020-09-01	2020	9

import pandas as pd
from tabulate import tabulate

# creating a DataFrame for Apartment(smaller units)

dataframe_apt_s = pd.DataFrame(df_smaller_apt)

  
# displaying the DataFrame
dataframe_apt_s
dataframe_apt_s.style.background_gradient()

dataframe_apt_s.style.highlight_max(color = 'yellow', axis = 0)
#print(tabulate(dataframe_condo, headers = 'keys', tablefmt = 'pretty'))

	Project Name	Street Name	Type	Postal District	Market Segment	Tenure	Type of Sale	No. of Units	Price ($)	Nett Price ($)	Area (Sqft)	Type of Area	Floor Level	Unit Price ($psf)	Date of Sale	Year	Month
88147	THOMSON IMPRESSIONS	LORONG PUNTONG	Apartment	20	RCR	99 yrs lease commencing from 2015	New Sale	1	745000	-	463	Strata	16 to 20	1610	2017-03-01 00:00:00	2017	3
50603	CENTRA HEIGHTS	SIMS AVENUE	Apartment	14	RCR	Freehold	Resale	1	693388	-	431	Strata	11 to 15	1610	2018-11-01 00:00:00	2018	11
37954	PRESTIGE HEIGHTS	BALESTIER ROAD	Apartment	12	RCR	Freehold	Resale	1	780000	-	484	Strata	06 to 10	1610	2021-10-01 00:00:00	2021	10
11033	PARC CLEMATIS	JALAN LEMPENG	Apartment	5	OCR	99 yrs lease commencing from 2019	New Sale	1	832000	-	517	Strata	06 to 10	1610	2019-08-01 00:00:00	2019	8
62734	THOMSON IMPRESSIONS	LORONG PUNTONG	Apartment	20	RCR	99 yrs lease commencing from 2015	Resale	1	745000	-	463	Strata	06 to 10	1610	2021-09-01 00:00:00	2021	9
72749	AFFINITY AT SERANGOON	SERANGOON NORTH AVENUE 1	Apartment	19	OCR	99 yrs lease commencing from 2018	New Sale	1	780000	-	484	Strata	01 to 05	1610	2020-03-01 00:00:00	2020	3
6286	CLAVON	CLEMENTI AVENUE 1	Apartment	5	OCR	99 yrs lease commencing from 2019	New Sale	1	849000	-	527	Strata	06 to 10	1610	2020-12-01 00:00:00	2020	12
55989	REZI 35	LORONG 35 GEYLANG	Apartment	14	RCR	Freehold	New Sale	1	727720	-	452	Strata	01 to 05	1610	2017-11-01 00:00:00	2017	11
78300	CASA AL MARE	JALAN LOYANG BESAR	Apartment	17	OCR	Freehold	New Sale	1	780000	-	484	Strata	01 to 05	1610	2019-01-01 00:00:00	2019	1
99125	HILLION RESIDENCES	JELEBU ROAD	Apartment	23	OCR	99 yrs lease commencing from 2013	New Sale	1	745000	-	463	Strata	16 to 20	1610	2017-10-01 00:00:00	2017	10
87725	THOMSON IMPRESSIONS	LORONG PUNTONG	Apartment	20	RCR	99 yrs lease commencing from 2015	New Sale	1	745000	-	463	Strata	06 to 10	1610	2017-04-01 00:00:00	2017	4
87328	STARS OF KOVAN	UPPER SERANGOON ROAD	Apartment	19	OCR	99 yrs lease commencing from 2015	New Sale	1	814420	-	506	Strata	16 to 20	1610	2017-05-01 00:00:00	2017	5
6563	CLAVON	CLEMENTI AVENUE 1	Apartment	5	OCR	99 yrs lease commencing from 2019	New Sale	1	849000	-	527	Strata	06 to 10	1610	2020-12-01 00:00:00	2020	12
54531	38 I SUITES	IPOH LANE	Apartment	15	RCR	Freehold	Resale	1	780000	-	484	Strata	11 to 15	1610	2018-03-01 00:00:00	2018	3
7498	WHISTLER GRAND	WEST COAST VALE	Apartment	5	OCR	99 yrs lease commencing from 2018	New Sale	1	814400	-	506	Strata	26 to 30	1610	2020-09-01 00:00:00	2020	9

# Plotting the bar graph - Condominiums (Smaller Home)
dataframe_apt_s.plot(kind='bar', x = 'Project Name', y = 'Price ($)', color = "skyblue", ec="grey", 
                     rot =90, fontsize = 16,figsize=(20,10), title = "Apartment - Smaller Homes" )
plt.xlabel('Project Name')
plt.ylabel('Price ($)')
plt.show()

Family Unit of 4 homes

# Analysis for family unit of 4 
df_family_u_4 = df_singapore_condo_apt_data[((df_singapore_condo_apt_data['Price ($)'].between(1000000,1600000, inclusive = True) & 
                              (df_singapore_condo_apt_data['Unit Price ($psf)'] ==1610) &
                              (df_singapore_condo_apt_data['Area (Sqft)'].between(850,1000, inclusive = True))))]
df_family_u_4

	Project Name	Street Name	Type	Postal District	Market Segment	Tenure	Type of Sale	No. of Units	Price ($)	Nett Price ($)	Area (Sqft)	Type of Area	Floor Level	Unit Price ($psf)	Date of Sale	Year	Month
11208	PARC CLEMATIS	JALAN LEMPENG	Apartment	5	OCR	99 yrs lease commencing from 2019	New Sale	1	1456000	-	904	Strata	11 to 15	1610	2019-08-01	2019	8
61292	PARC KOMO	UPPER CHANGI ROAD NORTH	Apartment	17	OCR	Freehold	New Sale	1	1560000	-	969	Strata	01 to 05	1610	2021-11-01	2021	11
93063	MIDWOOD	HILLVIEW RISE	Condominium	23	OCR	99 yrs lease commencing from 2018	New Sale	1	1438000	-	893	Strata	16 to 20	1610	2021-01-01	2021	1
50717	PARC ESTA	SIMS AVENUE	Apartment	14	RCR	99 yrs lease commencing from 2018	New Sale	1	1542000	-	958	Strata	06 to 10	1610	2018-11-01	2018	11
30636	CITYLIGHTS	JELLICOE ROAD	Condominium	8	RCR	99 yrs lease commencing from 2004	Resale	1	1438000	-	893	Strata	26 to 30	1610	2019-04-01	2019	4
9688	PARC CLEMATIS	JALAN LEMPENG	Apartment	5	OCR	99 yrs lease commencing from 2019	New Sale	1	1386000	-	861	Strata	11 to 15	1610	2019-12-01	2019	12
49783	PARC ESTA	SIMS AVENUE	Apartment	14	RCR	99 yrs lease commencing from 2018	New Sale	1	1473000	-	915	Strata	01 to 05	1610	2019-03-01	2019	3
11183	PARC CLEMATIS	JALAN LEMPENG	Apartment	5	OCR	99 yrs lease commencing from 2019	New Sale	1	1456000	-	904	Strata	11 to 15	1610	2019-08-01	2019	8
95662	DAIRY FARM RESIDENCES	DAIRY FARM LANE	Apartment	23	OCR	99 yrs lease commencing from 2018	New Sale	1	1507968	-	936	Strata	06 to 10	1610	2019-11-01	2019	11
8720	PARC CLEMATIS	JALAN LEMPENG	Apartment	5	OCR	99 yrs lease commencing from 2019	New Sale	1	1386000	-	861	Strata	16 to 20	1610	2020-06-01	2020	6
91156	MIDWOOD	HILLVIEW RISE	Condominium	23	OCR	99 yrs lease commencing from 2018	New Sale	1	1456000	-	904	Strata	16 to 20	1610	2021-07-01	2021	7
14630	REFLECTIONS AT KEPPEL BAY	KEPPEL BAY VIEW	Condominium	4	RCR	99 yrs lease commencing from 2006	Resale	1	1560000	-	969	Strata	21 to 25	1610	2018-06-01	2018	6

# Condominium for family Unit of 4
df_family_u_4_condominium = df_family_u_4[df_family_u_4['Type'].str.match('Condominium')] 
df_family_u_4_condominium

	Project Name	Street Name	Type	Postal District	Market Segment	Tenure	Type of Sale	No. of Units	Price ($)	Nett Price ($)	Area (Sqft)	Type of Area	Floor Level	Unit Price ($psf)	Date of Sale	Year	Month
93063	MIDWOOD	HILLVIEW RISE	Condominium	23	OCR	99 yrs lease commencing from 2018	New Sale	1	1438000	-	893	Strata	16 to 20	1610	2021-01-01	2021	1
30636	CITYLIGHTS	JELLICOE ROAD	Condominium	8	RCR	99 yrs lease commencing from 2004	Resale	1	1438000	-	893	Strata	26 to 30	1610	2019-04-01	2019	4
91156	MIDWOOD	HILLVIEW RISE	Condominium	23	OCR	99 yrs lease commencing from 2018	New Sale	1	1456000	-	904	Strata	16 to 20	1610	2021-07-01	2021	7
14630	REFLECTIONS AT KEPPEL BAY	KEPPEL BAY VIEW	Condominium	4	RCR	99 yrs lease commencing from 2006	Resale	1	1560000	-	969	Strata	21 to 25	1610	2018-06-01	2018	6

import pandas as pd
from tabulate import tabulate

# creating a DataFrame for Condominiums(Family Unit of 4)

dataframe_condo = pd.DataFrame(df_family_u_4_condominium)

  
# displaying the DataFrame
dataframe_condo
dataframe_condo.style.background_gradient()

dataframe_condo.style.highlight_max(color = 'lightgreen', axis = 0)
#print(tabulate(dataframe_condo, headers = 'keys', tablefmt = 'pretty'))

	Project Name	Street Name	Type	Postal District	Market Segment	Tenure	Type of Sale	No. of Units	Price ($)	Nett Price ($)	Area (Sqft)	Type of Area	Floor Level	Unit Price ($psf)	Date of Sale	Year	Month
93063	MIDWOOD	HILLVIEW RISE	Condominium	23	OCR	99 yrs lease commencing from 2018	New Sale	1	1438000	-	893	Strata	16 to 20	1610	2021-01-01 00:00:00	2021	1
30636	CITYLIGHTS	JELLICOE ROAD	Condominium	8	RCR	99 yrs lease commencing from 2004	Resale	1	1438000	-	893	Strata	26 to 30	1610	2019-04-01 00:00:00	2019	4
91156	MIDWOOD	HILLVIEW RISE	Condominium	23	OCR	99 yrs lease commencing from 2018	New Sale	1	1456000	-	904	Strata	16 to 20	1610	2021-07-01 00:00:00	2021	7
14630	REFLECTIONS AT KEPPEL BAY	KEPPEL BAY VIEW	Condominium	4	RCR	99 yrs lease commencing from 2006	Resale	1	1560000	-	969	Strata	21 to 25	1610	2018-06-01 00:00:00	2018	6

# Plotting the bar graph - Condominiums (Family Home)
dataframe_condo.plot(kind='bar', x = 'Project Name', y = 'Price ($)', color = "skyblue", ec="grey", 
                     rot =90, fontsize = 16,figsize=(20,10), title = "Condominiums - Family Home (Unit of 4)" )
plt.xlabel('Project Name')
plt.ylabel('Price ($)')
plt.show()

# Apartment for family Unit of 4
df_family_u_4_apt = df_family_u_4[df_family_u_4['Type'].str.match('Apartment')] 
df_family_u_4_apt

	Project Name	Street Name	Type	Postal District	Market Segment	Tenure	Type of Sale	No. of Units	Price ($)	Nett Price ($)	Area (Sqft)	Type of Area	Floor Level	Unit Price ($psf)	Date of Sale	Year	Month
11208	PARC CLEMATIS	JALAN LEMPENG	Apartment	5	OCR	99 yrs lease commencing from 2019	New Sale	1	1456000	-	904	Strata	11 to 15	1610	2019-08-01	2019	8
61292	PARC KOMO	UPPER CHANGI ROAD NORTH	Apartment	17	OCR	Freehold	New Sale	1	1560000	-	969	Strata	01 to 05	1610	2021-11-01	2021	11
50717	PARC ESTA	SIMS AVENUE	Apartment	14	RCR	99 yrs lease commencing from 2018	New Sale	1	1542000	-	958	Strata	06 to 10	1610	2018-11-01	2018	11
9688	PARC CLEMATIS	JALAN LEMPENG	Apartment	5	OCR	99 yrs lease commencing from 2019	New Sale	1	1386000	-	861	Strata	11 to 15	1610	2019-12-01	2019	12
49783	PARC ESTA	SIMS AVENUE	Apartment	14	RCR	99 yrs lease commencing from 2018	New Sale	1	1473000	-	915	Strata	01 to 05	1610	2019-03-01	2019	3
11183	PARC CLEMATIS	JALAN LEMPENG	Apartment	5	OCR	99 yrs lease commencing from 2019	New Sale	1	1456000	-	904	Strata	11 to 15	1610	2019-08-01	2019	8
95662	DAIRY FARM RESIDENCES	DAIRY FARM LANE	Apartment	23	OCR	99 yrs lease commencing from 2018	New Sale	1	1507968	-	936	Strata	06 to 10	1610	2019-11-01	2019	11
8720	PARC CLEMATIS	JALAN LEMPENG	Apartment	5	OCR	99 yrs lease commencing from 2019	New Sale	1	1386000	-	861	Strata	16 to 20	1610	2020-06-01	2020	6

import pandas as pd
from tabulate import tabulate

# creating a DataFrame for Condominiums(Family Unit of 4)

dataframe_apt = pd.DataFrame(df_family_u_4_apt)

  
# displaying the DataFrame
dataframe_apt
dataframe_apt.style.background_gradient()

dataframe_apt.style.highlight_max(color = 'yellow', axis = 0)
#print(tabulate(dataframe_condo, headers = 'keys', tablefmt = 'pretty'))

	Project Name	Street Name	Type	Postal District	Market Segment	Tenure	Type of Sale	No. of Units	Price ($)	Nett Price ($)	Area (Sqft)	Type of Area	Floor Level	Unit Price ($psf)	Date of Sale	Year	Month
11208	PARC CLEMATIS	JALAN LEMPENG	Apartment	5	OCR	99 yrs lease commencing from 2019	New Sale	1	1456000	-	904	Strata	11 to 15	1610	2019-08-01 00:00:00	2019	8
61292	PARC KOMO	UPPER CHANGI ROAD NORTH	Apartment	17	OCR	Freehold	New Sale	1	1560000	-	969	Strata	01 to 05	1610	2021-11-01 00:00:00	2021	11
50717	PARC ESTA	SIMS AVENUE	Apartment	14	RCR	99 yrs lease commencing from 2018	New Sale	1	1542000	-	958	Strata	06 to 10	1610	2018-11-01 00:00:00	2018	11
9688	PARC CLEMATIS	JALAN LEMPENG	Apartment	5	OCR	99 yrs lease commencing from 2019	New Sale	1	1386000	-	861	Strata	11 to 15	1610	2019-12-01 00:00:00	2019	12
49783	PARC ESTA	SIMS AVENUE	Apartment	14	RCR	99 yrs lease commencing from 2018	New Sale	1	1473000	-	915	Strata	01 to 05	1610	2019-03-01 00:00:00	2019	3
11183	PARC CLEMATIS	JALAN LEMPENG	Apartment	5	OCR	99 yrs lease commencing from 2019	New Sale	1	1456000	-	904	Strata	11 to 15	1610	2019-08-01 00:00:00	2019	8
95662	DAIRY FARM RESIDENCES	DAIRY FARM LANE	Apartment	23	OCR	99 yrs lease commencing from 2018	New Sale	1	1507968	-	936	Strata	06 to 10	1610	2019-11-01 00:00:00	2019	11
8720	PARC CLEMATIS	JALAN LEMPENG	Apartment	5	OCR	99 yrs lease commencing from 2019	New Sale	1	1386000	-	861	Strata	16 to 20	1610	2020-06-01 00:00:00	2020	6

# Plotting the bar graph - apartments (Family Home)
dataframe_apt.plot(kind='bar', x = 'Project Name', y = 'Price ($)', color = "teal", ec="grey", 
                     rot =90, fontsize = 16,figsize=(20,10), title = "Apartment - Family Home (Unit of 4)" )
plt.xlabel('Project Name')
plt.ylabel('Price ($)')
plt.show()

Other Observations from data analysis

Pairplot Analysis

• 'The Type' in terms of either Apartment or Condominium has been chosen in Pairplot Analysis.
• One such observation is for the Unit($psf), we can observe there are more apartments than condominiums for number of transactions within the districts

# Using pairplot to analyze Unit price($psf) 
# sns.pairplot(df_singapore_condo_apt_data, hue='Type', palette='coolwarm')

Scatter Plot Observation

# Dataframe with conditions created for better visualization 
#df_unit_v_pr = df_singapore_condo_apt_data[(df_singapore_condo_apt_data['Price ($)'].between(0,10000000, inclusive = True) & 
                                      #(df_singapore_condo_apt_data['Area (Sqft)'].between(0,6000, inclusive = True)))]
#df_unit_v_pr.head()

# Unit Price($psf) versus Price in the data analysis (Scatter Plot)
#sns.lmplot(y='Area (Sqft)', x='Price ($)', data=df_unit_v_pr,hue = "Type", palette= 'coolwarm', height=10, aspect=1.5)

# Unit Price($psf) versus Unit Price in the data analysis (Scatter Plot)
#sns.lmplot(y='Area (Sqft)', x='Unit Price ($psf)', data=df_unit_v_pr,hue = "Type", palette= 'coolwarm', height=10, aspect=1.5)